5 Q’s for David Kuehn, Program Manager for the Federal Highway Administration’s Exploratory Advances Research Program

The Center for Data Innovation spoke with David Kuehn, program manager for the Federal Highway Administration’s Exploratory Advanced Research (EAR) Program. Kuehn discussed how advancements in computer vision research could eventually make driving safer and stressed the value of knowledge sharing between academia, the government, and the private sector.

This interview has been edited.

Joshua New: The goal of the EAR Program to develop high-risk and high-payoff research in areas such as connected highway systems and performance assessment technologies. Could you discuss some of the most ambitious projects you have worked on?

David Kuehn: In 2009, the EAR Program sponsored a workshop on nanoscale science and engineering to see what advances could be applied to highways. Nanoscale sensors were one of several areas of opportunity discussed at the workshop. They are small enough to be embedded with minimal influence on what you are trying to measure, use so little power that they can scavenge the power from the environment, and can be deployed en masse so measurements can be more representative.

The program subsequently funded two projects on nanoscale sensors and two projects on energy harvesting. As you would expect, some results were better than others. But all of the projects had similar issues that limited our ability to mature the technology. While the sensors were novel, the communications components were not—they were still big and power hungry. And the sensors collected data, but the data did not clearly translate into useable information for automated controls, structural monitoring, or some other process the highway industry would find useful.

In 2012 the EAR Program again funded projects but this time looking at advancements at both the communication system and sensor level. The work is still underway, but we recently completed a review of the first year results and several of the projects look promising for making a significant impact on how transportation asset owners can manage their structures and pavements.

New: The EAR Program has recently completed and is currently working on several research projects related to computer vision. Why has computer vision become so important to the future of transportation?

Kuehn: With increased quality and capacity of cameras and communication systems, combined with decreasing costs, video of vehicle traffic has become pervasive. And video of traveler behavior—of drivers, cyclists, and pedestrians—is becoming common. Researchers studying areas such as system planning, operations, safety, and infrastructure condition assessment are now combining this video data with other data, such as location, vehicle dynamics, traffic signal state, and weather to understand how it can help. Though the research community is fortunate to be able to collect more and better data, the volume of this video data has the potential to overwhelm researchers’ capacity to analyze this data.

Currently, researchers need to manually code video data frame by frame. For a hour of data from four cameras at 24 frames per second, that is over 300,000 frames. Automating data extraction from video files is expected to dramatically reduce the costs of using this data, making it accessible to the widest possible pool of researchers. The EAR Program is working with the Office of Safety Research and Development on six research projects that are exploring breakthroughs in machine learning to automate extraction of safety data from driving studies such as the Strategic Highway Research Program project.

New: One of the EAR Program’s main objectives is to develop relationships between the public and private sectors that bridge research from academia, businesses, and the government. Why is this kind of knowledge sharing so important?

Kuehn: Diversity in research teams is an important consideration for us. With diversity comes different perspectives on how to solve questions, which is important for identifying new methods for resolving perennial research questions or considering emerging topics. Diversity can come from working in different disciplines and working in entirely different sectors. Academics should be good at understanding new research methods but may not understand what industry needs or have the ability to scale results so they are robust and cost effective. Many of our awards go to teams that include both academic and industry partners, and anecdotally, these teams seem to be more successful. There is a lot of research from other programs to suggests this is the case.

New: One of the EAR Program’s main research areas is cyber physical systems—what the general public would label as the Internet of Things. Many have criticized the current state of transportation infrastructure in the United States, and as policymakers work to address these problems, how should they prioritize integration of sensor networks? How could highway transportation benefit?

Kuehn: Sensing and communicating data is important, as many researchers and policy people want to know as much as they can about their systems. To successfully manage systems safely and efficiently requires more than just increased data. It requires a level of automation made possible with connected controls and data to allow humans to make decisions at higher levels of abstraction, which we are still much better at than computers.

As it relates to transportation, research and deployment of connected and automated transportation systems could result in reduction or elimination of deaths and serious injuries among all users of the transportation system, including drivers, passengers, cyclists, and pedestrians. It would also increase transportation system reliability and efficiency for the movement of both people and goods, as well as increase the resilience of this system so it could withstand weather and climate change impacts. And it would reduce the environmental and energy impacts in the development, operation, maintenance, and use of the transportation system.

There are many incumbent and new private entities in transportation working on automation and connectivity. The government brings a focus on the system as a whole, as well as public benefits, to the table. Many of the private entities are working on benefits at a vehicle or traveler level, which makes sense as their goal is to sell products and services to individuals. Many claim that these individual benefits will lead to overall system benefits, but it is not clear how these benefits will be distributed. This is why government is involved, to understand where there may be benefits or negative impacts to the system overall and to help industry advance the technology to improve the safety and efficiency of the transportation system as a whole.

New: Highways are just one aspect of transportation infrastructure. What would a fully networked transportation system, in which vehicles, roads, traffic management systems, and so on, could easily share data with one another, be capable of?

Kuehn:When the United States wanted to connect the country by railroad in the 19th century, the goal was to move people seamlessly. At the same time, the railroad also installed telegraph lines, which allowed information to flow across the country. This wasn’t the main goal, but it was a huge benefit of the rail system.

Similarly today the Federal Highway Administration and its state and local partners concentrate on the safe, efficient movement of people and goods on highways, as well as connecting with other modes of transportation such as rail, air, or maritime. To do so safely and efficiently requires data collection and dissemination at multiple points and geographical scales throughout the transportation system. This helps us to ensure vehicles stop for passing trains and helps us model transportation demand so we can better plan new regional or intercity infrastructure.

From a research perspective, the EAR program is investing in data fusion approaches and use of non-traditional data, which is data not initially collected for transportation purposes, for applications ranging from long distance passenger travel, goods movement, and roadway crashes. Some of this data is public, but some is private too. For example, some of this data is collected by or for transportation entities, such as transit fare box data, and other data comes from entities that provide wireless communications or point of sale services. With rapid transformation in the transportation industry that includes new players and businesses models, it will be an interesting time to research methods and incentives for secure interoperability and data exchange.